BIOMEDICAL ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
EEE5550	Computer Vision and Pattern Recognition	Fall	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi ZAFER İŞCAN
Recommended Optional Program Components:	None
Course Objectives:	The aim of the course is to study computer vision, which tries to “make computers see and interpret” using the observations in the form of multiple 2D (or 3D) images. In this undergraduate/graduate level course, the focus is on mainly basic computer vision concepts. The course will provide the participants with a background in Computer Vision both in practical aspects as being able to implement computer vision algorithms, and their mathematical understanding.

Learning Outcomes

The students who have succeeded in this course;
1.Discuss the main problems of computer vision, its uses and applications
2.Design various point-wise intensity transformations, neighborhood and spatial filtering operations over images
3.Transform images by geometric transforms: rigid, affine, and polynomial warping
4. Define and compute feature extraction operators: edges and corners, lines, and scale-invariant feature extraction in images
5. Describe mathematical image morphological operators
6. Construct segmentation algorithms: histogram-analysis-based thresholding, clustering intensities, and region growing
7. Discuss visual motion estimation methods
8. Apply object and shape recognition approaches to problems in computer vision
9. Describe basic multiple view geometry and stereo concepts

Course Content

Introduction to Computer Vision, Human Visual System, Pointwise Operations, Intensity Transformations, Histograms, Enhancement, Spatial Filtering, Neighborhood Operations, Edge Detection, Feature Extraction: Corners, Hough, Ellipse Fit, RANSAC, Correlation, SIFT, SURF, Morphological Image Processing, Segmentation: Adaptive Thresholding, Otsu, Region Growing, Active Contours, Segmentation: Adaptive Thresholding, Otsu, Region Growing, Active Contours, Introduction to Pattern Recognition, Review of Probability, Bayesian Decision Theory, Bayesian Estimation, PCA, kNN, SVM, Camera models, Camera Calibration, Stereopsis, Motion Estimation, Tracking

Weekly Detailed Course Contents

Week	Subject	Related Preparation
2)	Spatial Filtering Neighborhood Operations Edge Detection
3)	Feature Extraction: Corners, Hough, Ellipse Fit, RANSAC, Correlation
4)	Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF)
5)	Morphological Image Processing
6)	Segmentation: Adaptive Thresholding, Otsu, Region Growing, Active Contours
7)	Review, midterm exam
8)	Introduction to Pattern Recognition Review of Probability
9)	Bayesian Decision Theory
10)	Bayesian Estimation, PCA, kNN, SVM
11)	Camera models, Camera Calibration
12)	Stereopsis
13)	Motion Estimation
14)	Tracking

Sources

Course Notes / Textbooks:	Computer Vision: A Modern Approach (2nd Edition), David A. Forsyth and Jean Ponce, Prentice Hall, 2011. Digital Image Processing, R.C. Gonzalez, R.E. Woods, Pearson Prentice Hall 2008.
References:	Computer Vision, D. Ballard and C.M. Brown, Prentice Hall, online at: http://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/bandb.htm

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 10
Project	1	% 25
Midterms	1	% 25
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 35
PERCENTAGE OF FINAL WORK		% 65
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	4	56
Project	1	15	15
Homework Assignments	4	20	80
Midterms	1	3	3
Final	1	3	3
Total Workload			199

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge of subjects specific to mathematics (analysis, linear, algebra, differential equations, statistics), science (physics, chemistry, biology) and related engineering discipline, and the ability to use theoretical and applied knowledge in these fields in complex engineering problems.
2)	Identify, formulate, and solve complex Biomedical Engineering problems; select and apply proper modeling and analysis methods for this purpose
3)	Design complex Biomedical systems, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose.
4)	Devise, select, and use modern techniques and tools needed for solving complex problems in Biomedical Engineering practice; employ information technologies effectively.
5)	Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Biomedical Engineering.
6)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Biomedical Engineering-related problems.
7)	Ability to communicate effectively in Turkish, oral and written, to have gained the level of English language knowledge (European Language Portfolio B1 general level) to follow the innovations in the field of Biomedical Engineering; gain the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself.
9)	Having knowledge for the importance of acting in accordance with the ethical principles of biomedical engineering and the awareness of professional responsibility and ethical responsibility and the standards used in biomedical engineering applications
10)	Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11)	Acquire knowledge about the effects of practices of Biomedical Engineering on health, environment, security in universal and social scope, and the contemporary problems of Biomedical Engineering; is aware of the legal consequences of Mechatronics engineering solutions.